Abstract
Diabetes is one of the leading fatal diseases globally, putting a huge burden on the global healthcare system. Early diagnosis of diabetes is hence, of utmost importance and could save many lives. However, current techniques to determine whether a person has diabetes or has the risk of developing diabetes are primarily reliant upon clinical biomarkers. In this article, we propose a novel deep learning architecture to predict if a person has diabetes or not from a photograph of his/her retina. Using a relatively small-sized dataset, we develop a multi-stage convolutional neural network (CNN)-based model DiaNet that can reach an accuracy level of over 84% on this task, and in doing so, successfully identifies the regions on the retina images that contribute to its decision-making process, as corroborated by the medical experts in the field. This is the first study that highlights the distinguishing capability of the retinal images for diabetes patients in the Qatari population to the best of our knowledge. Comparing the performance of DiaNet against the existing clinical data-based machine learning models, we conclude that the retinal images contain sufficient information to distinguish the Qatari diabetes cohort from the control group. In addition, our study reveals that retinal images may contain prognosis markers for diabetes and other comorbidities like hypertension and ischemic heart disease. The results led us to believe that the inclusion of retinal images into the clinical setup for the diagnosis of diabetes is warranted in the near future.
Highlights
Diabetes mellitus or diabetes is considered as a collection of metabolic conditions that can predominantly be described by hyperglycemia rising from the deficiency in insulin discharge [1]
Though there are multiple studies [8], [9] that aim at detecting diabetic retinopathy from retinal images, none addressed the task of detecting diabetes using retinal images from a holistic point of view
All procedures were approved by the Institutional Review Board (IRB) of Hamad Medical Corporation, Qatar and only de-identified images were collected from Qatar Biobank (QBB).The dataset consists of retinal images from a diabetes cohort of size 246 and a control group of size 246
Summary
Diabetes mellitus or diabetes is considered as a collection of metabolic conditions that can predominantly be described by hyperglycemia rising from the deficiency in insulin discharge [1]. Provide visual cues for diabetes, most of the clinical guidelines recommended annual retinal screen for the diabetic patients through retinal fundus images or dilated eye examinations [5], [6]. These retinal images could be used to detect diabetes as well, but it requires subjective judgement from the ophthalmologist, and it might be time consuming as well. The human oriented subjective judgement could be avoided if we could implement the automation of retinal image-based diabetes diagnosis in clinical setup Such automation could alleviate the workload of the ophthalmologist as well as screen a large number of patients objectively within a short amount of time [7].
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